Synergizing deep learning-enabled preprocessing and human-AI integration for efficient automatic ground truth generation

Authors

Christopher Collazo, University of South Florida
Ian Vargas, University of South Florida
Brendon Cara, University of South Florida
Carla J Weinheimer, Washington University School of Medicine in St. Louis
Ryan P Grabau, University of South Florida
Dmitry Goldgof, University of South Florida
Lawrence Hall, University of South Florida
Samuel A Wickline, University of South Florida
Hua Pan, Washington University School of Medicine in St. Louis

Journal

Bioengineering

Publication Date

4-28-2024

Volume

11

Issue

5

First Page

434

Document Type

Open Access Publication

DOI

10.3390/bioengineering11050434

Rights and Permissions

Collazo C, Vargas I, Cara B, Weinheimer CJ, Grabau RP, Goldgof D, Hall L, Wickline SA, Pan H. Synergizing Deep Learning-Enabled Preprocessing and Human-AI Integration for Efficient Automatic Ground Truth Generation. Bioengineering (Basel). 2024 Apr 28;11(5):434. doi: 10.3390/bioengineering11050434 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/4.0/).

Recommended Citation

Collazo, Christopher; Vargas, Ian; Cara, Brendon; Weinheimer, Carla J; Grabau, Ryan P; Goldgof, Dmitry; Hall, Lawrence; Wickline, Samuel A; and Pan, Hua, "Synergizing deep learning-enabled preprocessing and human-AI integration for efficient automatic ground truth generation." Bioengineering. 11, 5. 434 (2024).
https://digitalcommons.wustl.edu/oa_4/3871

Department

ICTS (Institute of Clinical and Translational Sciences)

Additional Links

Supplemental material is available for this article at publisher site.